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Feature Subset Selection with Artificial Intelligence-Based Classification model for Biomedical Data

Computers, materials & Continua 2022年第9期72卷 4267-4281页

作者： Jaber S.Alzahrani Reem m.Alshehri mohammad Alamgeer anwer m.stafa hilal Abdelwahed motwakel Ishfaq Yaseen Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making *** latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification *** the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality *** resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)*** FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier ***,the FSS-AICBD technique undergoes min-max normalization technique to prevent data *** addition,the information gain(IG)approach is applied for the optimal selection of feature ***,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO *** choice of IG and GSO approaches results in promising medical data classification *** experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare *** simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

关键词： medical data classification feature selection deep learning healthcare sector artificial intelligence

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Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

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Computers, materials & Continua 2022年第4期71卷 825-842页

作者： mesfer Al Duhayyim marwa Obayya Fahd N.Al-Wesabi anwer m.stafa hilal mohammed Rizwanullah majdy m.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV ***-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different *** experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

关键词： Unmanned aerial vehicle 6G networks artificial intelligence energy efficiency clustering route planning data collection metaheuristics

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Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

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Computers, materials & Continua 2023年第3期74卷 6719-6736页

作者： Nabil Almalki mrim m.Alnfiai Fahd N.Al-Wesabi mesfer Alduhayyem anwer m.stafa hilal manar Ahmed Hamza King Salman Center for Disability Research RiyadhSaudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Science&ArtsKing Khaled UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the *** data collected can be leveraged to provide intelligent services through different *** is an extreme challenge to monitor disabled people from remote *** is because day-to-day events like falls heavily result in *** a person with disabilities,a fall event is an important cause of mortality and post-traumatic ***,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival *** current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with *** primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled *** attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature *** addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)*** the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet *** proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

关键词： Quality of living disabled persons intelligent models deep learning fall detection whale optimization algorithm

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Novel Image Encryption and Compression Scheme for IoT Environment

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Computers, materials & Continua 2022年第4期71卷 1443-1457页

作者： mesfer Al Duhayyim Fahd N.Al-Wesabi Radwa marzouk manar Ahmed Hamza anwer m.stafa hilal majdy m.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Latest advancements made in the processing abilities of smartdevices have resulted in the designing of Intelligent Internet of Things (IoT)environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless multimedia Surveillance Networks (WmSNs) form a vital part in IoT-assistedenvironment since it contains visual sensors that examine the surroundingsfrom a number of overlapping views by capturing the images *** IoT devices generate a massive quantity of digital media, it is thereforerequired to save the media, especially images, in a secure way. In order toachieve security, encryption techniques as well as compression techniques areemployed to reduce the amount of digital data, being communicated overthe network. Encryption Then Compression (ETC) techniques pave a wayfor secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presentsa new ETC technique to accomplish image security in IoT *** proposed model involves three major processes namely, IoT-based imageacquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NmWOA)abbreviated as ST-NmWOA. The optimal key generation of signcryptiontechnique takes place with the help of NmWOA. Besides, the presented modelalso uses Discrete Fourier Transform (DFT) and matrix minimization (mm)algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed *** obtained values infer that the presented model is superior in terms of bothcompression efficiency and data secrecy in resource-limited IoT environment.

关键词： Data compression image security encryption signcryption optimal key generation

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Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

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Computers, materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesabi Amani Abdulrahman Albraikan anwer m.stafa hilal majdy m.Eltahir manar Ahmed Hamza Abu Sarwar Zamani Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and *** recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop ***,earlier and precise apple leaf disease detection can minimize the spread of the *** works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf *** this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision *** proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal *** addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet ***,bidirectional long short term memory(BiLSTm)model is used as a classifier to determine the appropriate class labels of the apple leaf *** design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTm classifier shows the novelty of the *** range of experiments was performed to showcase the supremacy of the AIE-ALDC *** experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmentation

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Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications

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Computers, materials & Continua 2022年第6期71卷 6257-6273页

作者： majdy m.Eltahir Ibrahim Abunadi Fahd NAl-Wesabi anwer m.stafa hilal Adil Yousif Abdelwahed motwakel mesfer Al Duhayyim manar Ahmed Hamza Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Novel coronavirus 2019(COVID-19)has affected the people’s health,their lifestyle and economical status across the *** application of advanced Artificial Intelligence(AI)methods in combination with radiological imaging is useful in accurate detection of the *** also assists the physicians to take care of remote villages *** current research paper proposes a novel automatedCOVID-19 analysismethod with the help ofOptimal Hybrid Feature Extraction(OHFE)and Optimal Deep Neural Network(ODNN)called OHFE-ODNN from chest x-ray *** objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray *** presented OHFE-ODNN method includes a sequence of procedures such as median Filtering(mF)-based pre-processed,feature extraction and finally,binary(COVID/Non-COVID)and multiclass(Normal,COVID,SARS)***,in OHFE-based feature extraction,Gray Level Co-occurrence matrix(GLCm)and Histogram of Gradients(HOG)are integrated *** presented OHFE-ODNN model includes Squirrel Search Algorithm(SSA)for finetuning the parameters of *** performance of the presented OHFEODNN technique is conducted using chest x-rays *** presented OHFE-ODNN method classified the binary classes effectively with a maximumprecision of 95.82%,accuracy of 94.01%and F-score of 96.61%.Besides,multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%,accuracy of 95.60%and an F-score of 95.73%.

关键词： COVID-19 classification deep learning radiological images

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Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

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Intelligent Automation & Soft Computing 2023年第6期36卷 2639-2652页

作者： mrim m.Alnfiai Nabil Almalki Fahd N.Al-Wesabi mesfer Alduhayyem anwer m.stafa hilal manar Ahmed Hamza King Salman Center for Disability Research Riyadh13369Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Science College of Science&Arts at MuhayelKing Khaled UniversityAbha62217Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Text-To-Speech(TTS)is a speech processing tool that is highly helpful for visually-challenged *** TTS tool is applied to transform the texts into human-like ***,it is highly challenging to accomplish the TTS out-comes for the non-diacritized text of the Arabic language since it has multiple unique features and *** special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic ***,such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context *** this background,the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer(ODLD-ATSS)model to help the visually-challenged people in the Kingdom of Saudi *** prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged *** attain this,the presented ODLD-ATSS model initially designs a Gated Recurrent Unit(GRU)-based prediction model for diacritic and gemination ***,the Buckwalter code is utilized to capture,store and display the Arabic *** improve the TSS performance of the GRU method,the Aquila Optimization Algorithm(AOA)is used,which shows the novelty of the *** illustrate the enhanced performance of the proposed ODLD-ATSS model,further experi-mental analyses were *** proposed model achieved a maximum accu-racy of 96.35%,and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models.

关键词： Saudi Arabia visually challenged people deep learning Aquila optimizer gated recurrent unit

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IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

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Computer Systems Science & Engineering 2023年第7期46卷 475-489页

作者： mesfer Alduhayyem mrim m.Alnfiai Nabil Almalki Fahd N.Al-Wesabi anwer m.stafa hilal manar Ahmed Hamza Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia King Salman Center for Disability Research RiyadhAl-Hayātim 16273Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Science College of Science&ArtsKing Khaled UniversityAr-Riyad 12372Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Visual impairment is one of the major problems among people of all age groups across the *** Impaired Persons(VIPs)require help from others to carry out their day-to-day *** they experience several problems in their daily lives,technical intervention can help them resolve the *** this background,an automatic object detection tool is the need of the hour to empower VIPs with safe *** recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it *** current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help *** primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to *** data acquisition,IoT devices are used in this ***,the Lightweight RetinaNet(LWR)model is applied to detect objects ***,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR ***,the Long Short-Term memory(LSTm)model is exploited for classifying *** performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct *** comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs.

关键词： Visually impaired people deep learning object detection computer vision long short-term memory transient search optimization

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Deep Learning Enabled microarray Gene Expression Classification for Data Science Applications

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Computers, materials & Continua 2022年第11期73卷 4277-4290页

作者： Areej A.malibari Reem m.Alshehri Fahd N.Al-Wesabi Noha Negm mesfer Al Duhayyim anwer m.stafa hilal Ishfaq Yaseen Abdelwahed motwakel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In bioinformatics applications,examination of microarray data has received significant interest to diagnose *** gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of *** data classification incorporates multiple disciplines such as bioinformatics,machine learning(mL),data science,and pattern *** paper designs an optimal deep neural network based microarray gene expression classification(ODNN-mGEC)model for bioinformatics *** proposed ODNN-mGEC technique performs data normalization process to normalize the data into a uniform ***,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical ***,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)*** utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification *** examining the improved outcomes of the ODNN-mGEC technique,a wide ranging experimental analysis is made against benchmark *** extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-mGEC technique in terms of different measures.

关键词： Bioinformatics data science microarray gene expression data classification deep learning metaheuristics

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Competitive multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

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Computer Systems Science & Engineering 2023年第5期45卷 1249-1263页

作者： anwer m.stafa hilal Amal Al-Rasheed Jaber SAlzahrani majdy m.Eltahir mesfer Al Duhayyim Nermin m.Salem Ishfaq Yaseen Abdelwahed motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Sleep plays a vital role in optimum working of the brain and the *** people suffer from sleep-oriented illnesses like apnea,insomnia,*** stage classification is a primary process in the quantitative examination of polysomnographic *** stage scoring is mainly based on experts’knowledge which is laborious and time ***,it can be essential to design automated sleep stage classification model using machine learning(mL)and deep learning(DL)*** this view,this study focuses on the design of Competitive multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CmVODL-SSC)model using Electroencephalogram(EEG)*** proposed CmVODL-SSC model intends to effectively categorize different sleep stages on EEG ***,data pre-processing is performed to convert the actual data into useful ***,a cascaded long short term memory(CLSTm)model is employed to perform classification *** last,the CmVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTm *** order to report the enhancements of the CmVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CmVODL-SSC model with average accuracy of 96.90%.

关键词： Signal processing EEG signals sleep stage classification clstm model deep learning cmvo algorithm

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